Abstract
Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system, progressing from Relapsing-Remitting MS (RRMS) to Secondary Progressive MS (SPMS) in many cases. The transition involves complex biological changes. Our study aims to identify potential biomarkers for distinguishing SPMS by analyzing gene expression differences between normal-appearing and lesioned parietal grey matter, which may also contribute to understand the pathogenesis of SPMS. We utilized public datasets from the Gene Expression Omnibus (GEO), applying bioinformatics and machine learning techniques including Weighted Gene Co-expression Network Analysis (WGCNA), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) enrichment analysis, protein-protein interaction (PPI) networks, the Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF) for predictive model construction. Our study also included analyses of immune cell infiltration. The study identified 359 DEGs, with 105 up-regulated and 254 down-regulated. WGCNA identified 264 common genes, which were subjected to KEGG and GO enrichment analyses, highlighting their role in immune response and viral infection pathways. Four genes (BCL3, GBP1, IFI16, and CCR1) were identified as key biomarkers for SPMS, supported by LASSO regression and RF analyses. These genes were further validated through receiver operating characteristic (ROC) curves, demonstrating significant predictive potential for SPMS. Our study provides a novel set of biomarkers for SPMS from lesioned grey matter of SPMS cases, offering potential for diagnosis and targeted therapeutic strategies. The identified biomarkers link closely with SPMS pathology, especially regarding immune system modulation.